CN116840805B - Human vital sign detection method based on MIMO radar and beam forming - Google Patents
Human vital sign detection method based on MIMO radar and beam forming Download PDFInfo
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Abstract
The invention discloses a human vital sign detection method based on MIMO radar and beam forming. The human vital sign detection method based on MIMO radar and beam forming comprises the following steps: extracting a distance-angle unit of the measured object; acquiring Doppler frequency spectrum; and estimating the heart rate value and the respiration value of the tested object, and detecting the vital signs of the human body of the tested object. According to the invention, each distance-angle unit of a measured object is extracted through preprocessing the received initial echo signals, then the corresponding phase signals are extracted, the Doppler frequency spectrum is obtained through unwrapping and differential processing, then the Doppler frequency spectrum is analyzed, a respiratory pseudo spectrum is constructed in a preset respiratory frequency range to obtain the respiratory value of the measured object, finally a heart rate pseudo spectrum is constructed in a preset heart rate range, and the heart rate value of the measured object is obtained by combining the heart rate harmonic product spectrum, so that the improvement of the detection accuracy of the vital signs of the human body is achieved, and the problem of low detection accuracy of the vital signs of the human body in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of vital sign detection, in particular to a human vital sign detection method based on MIMO radar and beam forming.
Background
Human vital sign detection involves measuring a series of physiological parameters that are indicative of the health condition of an individual. Vital signs such as respiration and heart rate are very useful in locating and managing vital health problems and the like. Heart rate measurement techniques have evolved from counting pulses to contact-based methods such as electrocardiography and photoplethysmography. Currently, millimeter wave radar sensors with low cost and easy integration make noncontact weak vital sign signal detection very promising. Multiple-input multiple-output (Multiple Input Multiple Output, MIMO) radar refers to multiple radiating and receiving stations. According to diversity technology, each receiving antenna element should receive different information, so as to improve the overall performance of the system, such as link quality, radar detection probability or positioning accuracy. From a processing perspective, MIMO radar allows for Digital Beamforming (DBF) at reception. However, radar echo signals reflected from the mechanical behavior of the heart (i.e. the heart beat) are complex and mixed with other stronger mechanical signals (e.g. respiration, body motion, etc.), and thus contain several different doppler frequencies that are directly related to the respiration and heart rate modes.
In the prior art, the conventional spectrum-based method for processing the radar echo signals cannot accurately estimate the frequencies, such as harmonics and cross products of the respiratory signals, so that the robust detection of the heart rate signals is complicated. And determining the position information of the tested person by using a distance selection strategy of the radar maximum echo signal and the maximum variance, so that the measurement stability of the radar is not high.
For example, bulletin numbers: the invention patent of CN114742117B discloses a human vital sign detection method of a millimeter wave radar in a complex indoor scene, which comprises the following steps: training the classifier according to the sample data; performing ADC data acquisition on millimeter wave radar echo signals, performing FFT processing, static clutter filtering and CFAR and DOA estimation after sampling data are obtained, and obtaining point cloud data; carrying out Doppler transformation and feature extraction on the point cloud data, and filtering out moving points with large motion amplitude; extracting phase information from the signals of N continuous frames of the point cloud, and estimating respiratory and heartbeat frequencies; extracting features of the point cloud signals of the continuous N frames; inputting the extracted features into a classifier for prediction; outputting detected human body target respiratory heartbeat data in scene
For example, bulletin numbers: the invention patent of CN111481184B discloses a multi-target respiratory rate monitoring method and system based on millimeter wave radar technology, which comprises multi-target respiratory rate signal separation and heart rate and respiratory rate extraction, and the detection of the non-contact heart rate and respiratory rate is carried out through a millimeter wave radar module, so that a fussy contact type monitoring program is avoided, meanwhile, the privacy of a detected person is not violated, the detection precision is improved, and the measurement reliability is greatly enhanced.
However, in the process of implementing the technical scheme of the embodiment of the application, the inventor discovers that the above technology has at least the following technical problems:
in the prior art, other interference frequencies are mixed by processing radar echo signals based on frequency spectrums by a traditional method, the position of vital signs is inaccurately determined by a distance selection strategy of the maximum echo signals and the maximum variance of the radar, and the problem of low detection accuracy of human vital signs exists.
Disclosure of Invention
The human vital sign detection method based on MIMO radar and beam forming solves the problem of low human vital sign detection accuracy in the prior art, and improves the human vital sign detection accuracy.
The embodiment of the application provides a human vital sign detection method based on MIMO radar and beam forming, which is used for a server and comprises the following steps: s1, a receiving station of the MIMO radar receives initial echo signals reflected back by a radiation station after transmitting waveforms to a measured object through a preset number of antennas from different angles and distances in a preset time window, and then performs preprocessing operation on the initial echo signals to extract distance-angle units of the measured object; s2, extracting phase signals of a distance-angle unit of the measured object, then performing unwrapping and differential processing on the phase signals of the distance-angle unit of the measured object, and performing fast Fourier transform processing by combining a fast Fourier transform algorithm to obtain Doppler frequency spectrum; s3, analyzing the acquired Doppler frequency spectrum, screening the Doppler frequency spectrum to obtain a respiratory signal and a heart rate signal, estimating the heart rate value and the respiratory value of the tested object according to the obtained respiratory signal and heart rate signal, detecting the human vital sign of the tested object according to the estimated heart rate value and the respiratory value, and calculating the human vital sign detection efficiency.
Further, the pretreatment operation in S1 specifically includes the following steps: s11, static clutter filtering: according to a phasor mean value cancellation algorithm, an intermediate frequency signal of an initial echo signal is removed through average processing in a slow time dimension and a fast time dimension, and an echo time domain signal is obtained; s12, obtaining distance information: according to a fast Fourier transform algorithm, converting the echo time domain signal into an echo frequency domain distance signal by performing distance dimension fast Fourier transform on the echo time domain signal, and obtaining distance information of the measured object according to the echo frequency domain distance signal; s13, acquiring angle information: according to a fast Fourier transform algorithm, converting the echo time domain signal into an echo frequency domain angle signal by performing angle dimension fast Fourier transform on the echo time domain signal, and obtaining angle information of the measured object according to the echo frequency domain angle signal; s14, collecting distance-angle units of the measured object: and comparing the distance information of the measured object and the angle information of the measured object with the reference distance information and the reference angle information to obtain a target distance width and a target angle width, setting a distance window and an angle window according to the target distance width and the target angle width, and collecting distance-angle units of the measured object.
Further, the specific extraction process of the phase signals of the plurality of distance-angle units of the measured object in S2 is as follows: obtaining a distance unit with the highest echo frequency domain distance signal power according to the antenna dimension fast Fourier transform result through a formula, and then obtaining the distance with the highest echo frequency domain distance signal powerSelecting a distance unit with the smallest echo frequency domain distance signal and a distance unit with the largest echo frequency domain distance signal as the center; combining the minimum distance unit and the maximum distance unit of the echo frequency domain distance signals, obtaining the angle unit with the highest echo frequency domain angle signal power through a formula, and selecting the angle unit with the minimum echo frequency domain angle signal and the angle unit with the maximum echo frequency domain angle signal power as the center, thereby obtaining the echo signals with the distances v, the angles u and the time windows m after the distance dimension fast Fourier transform and the angle dimension fast Fourier transformThe method comprises the steps of carrying out a first treatment on the surface of the Extracting echo signal->And extracting a phase signal of the slow time dimension signal.
Further, the specific acquisition process of the distance unit with the smallest distance signal and the distance unit with the largest distance signal of the echo frequency domain is as follows: the index of the distance cell is denoted by V, Representing echo frequency domain distance signals with distance v, angle u and time window m after distance dimension fast Fourier transform and angle dimension fast Fourier transform, and obtaining a distance unit with highest power according to a power value through a formula>The specific calculation formula is as follows:
wherein M is the maximum value of the time window, +.>The number of points for the antenna dimension fast fourier transform; for->Is +.>Selecting distance units to obtain minimum distance unit index +.>And maximum distance element index->。
Further, the specific acquisition process of the minimum angle unit and the maximum angle unit of the echo frequency domain angle signal is as follows: the index of the angular unit is denoted by U,representing echo frequency domain angle signals with distance v, angle u and time window m after distance dimension fast Fourier transform and angle dimension fast Fourier transform, and selecting an angle unit with highest power according to a power value through a formula>The specific calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the For->Is +.>Selecting distance units to obtain minimum angle unit index +.>And maximum angular element index +.>。
Further, the specific estimation process of the respiration value of the tested object is as follows: s311, calculating the respiration estimated value of each distance-angle unit of the measured object by selecting the maximum value position of the Doppler frequency spectrum in the preset respiration frequency range, and obtaining a respiration matrix R by the respiration estimated value of each distance-angle unit; s312, constructing a respiration pseudo spectrum according to the respiration estimated value in the recorded respiration matrix R and the occurrence frequency of the respiration estimated value, and taking the respiration estimated value with the highest occurrence frequency in the respiration pseudo spectrum as the respiration value of the tested object.
Further, the specific estimation process of the heart rate value of the measured object is as follows: s321, calculating heart rate estimated values of all distance-angle units of the measured object by selecting the maximum value position of the Doppler frequency spectrum in a preset heart rate frequency range, and obtaining a heart rate matrix H by the heart rate estimated values of all the distance-angle units; s322, judging whether each heart rate estimated value in the heart rate matrix H and the respiration estimated value in the respiration matrix R have a multiple relation: if the heart rate estimated value has a multiple relation with the respiration estimated value in the respiration matrix R, ignoring the heart rate estimated value, and continuing to judge the next heart rate estimated value; if the heart rate estimation value has no multiple relation with the respiration estimation value in the respiration matrix R, S323 is executed; s323, recording a heart rate estimated value and the occurrence frequency of the heart rate estimated value when the heart rate estimated value and the respiration estimated value in the respiration matrix R have no multiple relation, and constructing a heart rate pseudo spectrum according to the recorded heart rate estimated value and the occurrence frequency of the heart rate estimated value in the heart rate matrix H; s324, obtaining a heart rate estimated value with highest occurrence frequency in a heart rate pseudo spectrum, and judging whether the heart rate estimated value is smaller than a preset heart rate: if the heart rate estimated value is smaller than the preset heart rate, the heart rate estimated value is the heart rate value of the tested object; if the heart rate estimation value is not less than the preset heart rate, performing S325; s325, regenerating a reference heart rate pseudo spectrum through a heart rate harmonic product spectrum, acquiring a reference heart rate estimated value with highest occurrence frequency in the reference heart rate pseudo spectrum, and executing S324 until obtaining the heart rate value of the tested object.
Further, the specific construction process of the heart rate harmonic product spectrum is as follows: according to the discrete Fourier transform algorithm, performing discrete Fourier transform processing on the heart rate pseudo spectrum, and extracting heart rate estimated values for multiplication to obtain a heart rate harmonic product spectrumHeart rate harmonic product spectrum->The specific calculation formula of (2) is as follows: />Wherein q is the decimated magnitude spectrum +.>Is (are) extracted by the extraction rate of->Q is a preset harmonic number, and f is a heart rate frequency.
Further, the specific process of generating the reference heart rate pseudo spectrum and obtaining the reference heart rate estimated value with the highest occurrence frequency in the reference heart rate pseudo spectrum is as follows: the specific generation process of the reference heart rate pseudo spectrum comprises the following steps: by selecting heart rate harmonic product spectrum within preset heart rate frequency rangeCalculating the reference heart rate estimated value of each distance-angle unit of the measured object, and obtaining the heart rate harmonic product spectrum of the extraction rate q from the reference heart rate estimated value of each distance-angle unit>Is>The method comprises the steps of carrying out a first treatment on the surface of the Judging reference heart rate matrix->Whether the reference heart rate estimated value and the respiration estimated value in the respiration matrix R have a multiple relation: if the reference heart rate estimated value has a multiple relation with the respiration estimated value in the respiration matrix R, ignoring the reference heart rate estimated value, continuously judging the next reference heart rate estimated value, otherwise, recording the frequency of occurrence of the reference heart rate estimated value and the reference heart rate estimated value; according to the recorded reference heart rate matrix- >Constructing a reference heart rate pseudo spectrum according to the reference heart rate estimated value and the occurrence frequency of the reference heart rate estimated value; the specific acquisition process of the reference heart rate estimated value with the highest occurrence frequency in the reference heart rate pseudo spectrum is as follows: harmonic product spectrum of heart rate->Analyzing, namely obtaining a reference heart rate estimated value (which is highest in occurrence frequency) in a heart rate harmonic product spectrum through a formula in a preset heart rate frequency range>Reference heart rate estimate +.>The specific calculation formula of (2) is as follows: />。
Further, the specific calculation process of the human vital sign detection efficiency is as follows: based on calculating the single distance unit and the distance unit with highest powerTime for distance of +.>And calculating the angle unit with the highest power and the single angle unit +.>Time taken for distance +.>The number of combined distance units->And the number of angle units->Time for obtaining echo signal +.>The reference time for obtaining the echo signal is +.>The method comprises the steps of carrying out a first treatment on the surface of the Acquiring time +.>And the post-processing time of the echo signal +.>The corresponding reference times are +.>And->The method comprises the steps of carrying out a first treatment on the surface of the The human vital sign detection efficiency DP is calculated through a formula, and the specific calculation formula is as follows:
wherein e is a natural constant, < > >And->And obtaining a correction factor of the time for obtaining the echo signal and a correction factor of the total time of the distance-angle unit of the measured object by post-processing of the echo signal and extracting the correction factors of the time for obtaining the echo signal and the total time of the distance-angle unit of the measured object respectively for the number of the distance units and the angle units.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. obtaining a distance-angle unit of a measured object through preprocessing a received initial echo signal, extracting a phase signal of the distance-angle unit of the measured object, performing unwrapping, differential processing and fast Fourier transformation to obtain a Doppler frequency spectrum, screening the Doppler frequency spectrum to obtain a respiratory signal and a heart rate signal, respectively constructing a respiratory pseudo spectrum and a heart rate pseudo spectrum in a preset respiratory frequency range and a preset heart rate range, obtaining a respiratory value of the measured object by the respiratory pseudo spectrum, finally obtaining a heart rate value of the measured object by combining a reference heart rate pseudo spectrum generated by a heart rate harmonic product spectrum, and detecting vital signs according to the respiratory value and the heart rate value of the measured object, thereby realizing accurate detection of the vital signs of a human body, further realizing improvement of detection accuracy of the vital signs of the human body, and effectively solving the problem of low detection accuracy of the vital signs of the human body in the prior art;
2. Obtaining echo time domain signals through average processing of received initial echo signals in slow time dimension and fast time dimension, obtaining distance information of a measured object through fast Fourier transform processing of distance dimension, obtaining angle information of the measured object through fast Fourier transform processing of angle dimension, obtaining a plurality of distance-angle units of the measured object through setting a distance window and an angle window according to comparison results of the distance information and the angle information, and obtaining respiratory value and heart rate value of the measured object through processing of the extracted distance-angle units, so that stable detection of vital signs of a human body is achieved, and further improvement of detection stability of vital signs of the human body is achieved;
3. obtaining a minimum distance unit and a maximum distance unit of an echo frequency domain distance signal through the number of points of the antenna dimension fast Fourier transform, then combining the obtained minimum distance unit and the obtained maximum distance unit of the echo frequency domain distance signal to obtain a minimum angle unit and a maximum angle unit of an echo frequency domain angle signal, thereby obtaining echo signals after the distance dimension fast Fourier transform and the angle dimension fast Fourier transform, further obtaining corresponding phase signals through slow time dimension signals of the extracted echo signals, processing the phase signals to obtain Doppler frequency spectrums, and finally obtaining heart rate values and respiratory values of a tested object through analysis of the Doppler frequency spectrums, thereby realizing the fast detection of vital signs of a human body and further realizing the improvement of the detection efficiency of vital signs of the human body.
Drawings
Fig. 1 is a flowchart of a human vital sign detection method based on MIMO radar and beam forming according to an embodiment of the present application;
FIG. 2 is a flowchart of obtaining a distance-angle unit of a measured object according to an embodiment of the present application;
FIG. 3 is a flowchart showing an embodiment of the present application for estimating a respiration value and a heart rate value of a subject;
FIG. 4 is a schematic diagram of generating a heart rate harmonic product spectrum according to an embodiment of the present application;
fig. 5 is a diagram illustrating an exemplary spectrum of a sine wave with an analog fundamental frequency of 1Hz and a harmonic product spectrum thereof according to an embodiment of the present application.
Detailed Description
According to the embodiment of the application, the problem of low detection accuracy of human vital signs in the prior art is solved by providing the human vital sign detection method based on the MIMO radar and the beam forming, the initial echo signals reflected by the radiation station are received by a preset number of antennas in the receiving station of the MIMO radar in a preset time window and are preprocessed to extract the distance-angle units of the detected object, then the phase signals of the distance-angle units of the detected object are extracted and are subjected to unwrapping and differential processing, the Doppler frequency spectrum is obtained by combining a fast Fourier transform algorithm, the respiratory signals and the heart rate signals are obtained by screening the Doppler frequency spectrum, and finally the heart rate value and the respiratory value of the detected object are estimated according to the respiratory signals and the heart rate signals so as to detect the human vital signs of the detected object, so that the detection accuracy of the human vital signs is improved.
The technical scheme in the embodiment of the application aims to solve the problem of low accuracy of human vital sign detection, and the overall thought is as follows:
the method comprises the steps of receiving initial echo signals reflected by a radiation station through an antenna in a receiving station of the MIMO radar, extracting a plurality of distance-angle units of a measured object through static clutter filtering, distance dimension FFT, angle dimension FFT, distance window and angle window of the initial echo signals, extracting phase signals of the plurality of distance-angle units of the measured object, performing unwrapping and differential processing to obtain Doppler frequency spectrums, analyzing the Doppler frequency spectrums, constructing a respiration pseudo spectrum in a preset respiration frequency range to obtain respiration values of the measured object, constructing a heart rate pseudo spectrum in a preset heart rate range, and combining a reference heart rate pseudo spectrum generated by the heart rate harmonic product spectrum to obtain heart rate values of the measured object.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
As shown in fig. 1, a flowchart of a human vital sign detection method based on MIMO radar and beam forming according to an embodiment of the present application is used for a server, and the method includes the following steps: s1, extracting a distance-angle unit of a measured object: receiving initial echo signals reflected back by a radiation station after transmitting waveforms to a measured object by a receiving station of the MIMO radar through a preset number of antennas from different angles and distances in a preset time window, and then preprocessing the initial echo signals to extract a distance-angle unit of the measured object; s2, doppler frequency spectrum is acquired: extracting phase signals of a distance-angle unit of a measured object, then performing unwrapping and differential processing on the phase signals of the distance-angle unit of the measured object, and performing fast Fourier transform processing by combining a fast Fourier transform algorithm to obtain Doppler frequency spectrum; s3, estimating heart rate value and respiratory value of the tested object, and detecting vital signs of the human body of the tested object: analyzing the acquired Doppler frequency spectrum, screening the Doppler frequency spectrum to obtain a respiratory signal and a heart rate signal, estimating the heart rate value and the respiratory value of the tested object according to the obtained respiratory signal and heart rate signal, detecting the human vital sign of the tested object according to the estimated heart rate value and the respiratory value, and calculating the detection efficiency of the human vital sign.
In the present embodiment, the multiple input multiple output (Multiple Input Multiple Output, MIMO) radar refers to a radar with a multiple input multiple output antenna system, for example, one-transmit-eight-receive is equivalent to two-transmit-four-receive antennas, which has the advantage that the angular resolution (spatial resolution) of the radar can be improved; the purpose of the unwrapping is to incorporate phase jumps; the phase jump means that discontinuous abrupt change occurs in the phase of the signal, and the phase value suddenly jumps from one period to another period, so that the phase information cannot be directly interpreted and used correctly; unwrapping is a signal processing technique that eliminates phase discontinuities by continuously adding or subtracting integer multiples of 2pi from phase jumps to accurately analyze and process the signal; heart rate and respiration are important physiological indicators of vital signs of the human body; for continuous data streams, a fixed observation time window is required to collect enough frames for measurement; a larger temporal observation window corresponds to a better doppler resolution, since the corresponding signal contains more respiratory and heart rate cycles; the improvement of the detection accuracy of the vital signs of the human body is realized.
Further, as shown in fig. 2, in a flowchart for acquiring a distance-angle unit of a measured object according to an embodiment of the present application, the preprocessing operation in S1 includes the following specific steps: s11, static clutter filtering: according to a phasor mean value cancellation algorithm, an intermediate frequency signal of an initial echo signal is removed through average processing in a slow time dimension and a fast time dimension, and an echo time domain signal is obtained; s12, obtaining distance information: according to a fast Fourier transform algorithm, converting the echo time domain signal into an echo frequency domain distance signal by performing distance dimension fast Fourier transform on the echo time domain signal, and obtaining distance information of the measured object according to the echo frequency domain distance signal; s13, acquiring angle information: according to a fast Fourier transform algorithm, converting the echo time domain signal into an echo frequency domain angle signal by performing angle dimension fast Fourier transform on the echo time domain signal, and obtaining angle information of the measured object according to the echo frequency domain angle signal; s14, collecting distance-angle units of the measured object: and comparing the distance information of the measured object and the angle information of the measured object with the reference distance information and the reference angle information to obtain a target distance width and a target angle width, setting a distance window and an angle window according to the target distance width and the target angle width, and collecting distance-angle units of the measured object.
In this embodiment, the distance-angle unit that acquires the object to be measured is the first part of human vital sign detection, i.e. the object to be measured is located in the distance and angle domain,is an initial echo signal with a distance v, an angle u and a time window m after being subjected to a distance dimension fast Fourier transform (Fast Fourier Transform, FFT) and an angle dimension FFT; static clutter removal is to filter static targets from frequency spectrums so as to ensure that reflection of static clutter from the environment is inhibited, and the basic principles of a common static clutter filtering method are a zero-speed channel zero-setting method, a moving target display and a phasor average value cancellation algorithm respectively; the core idea of the phasor mean value cancellation algorithm is to calculate the mean value and make the difference, and the signal to noise ratio of the moving target or the inching target is greatly improved while the phase of the static target is restrained by calculating the mean value; the slow time and fast time dimensions refer to the column signal and the row signal, respectively; the distance dimension FFT is implemented by performing FFT conversion on radar echo signals in the time domain; the distance information is encoded in a spectrum, each spectral point corresponding to a different distance; the angle dimension FFT is used for estimating the direction and angle information of radar echo signals, the angle information is extracted based on the phase difference of signals received between different antennas, and an angle spectrogram or an angle-distance matrix can be obtained through the angle dimension FFT; the target distance width and the target angle width represent the distance width and the angle width that one wishes to utilize; the initial echo signals can be regarded as a data cube, the number of data points in the data cube can be limited by the steps of setting a distance window and an angle window, and echo signals exceeding the target range in distance and angle are removed, so that the size of the data cube is reduced; the distance window only retains echo signals falling within the window range; the angle window only retains the target echo signals within the window range; the complexity of human vital sign detection is reduced.
Further, the specific extraction process of the phase signals of the plurality of distance-angle units of the measured object in S2 is as follows: acquiring a distance unit with highest echo frequency domain distance signal power through a formula according to the antenna dimension fast Fourier transform result, and selecting a distance unit with smallest echo frequency domain distance signal and a distance unit with largest echo frequency domain distance signal by taking the distance unit with highest echo frequency domain distance signal power as a center; combining echo frequency domainsObtaining an angle unit with the highest echo frequency domain angle signal power through a formula by using a distance unit with the smallest distance signal and a distance unit with the largest distance signal, selecting the angle unit with the smallest echo frequency domain angle signal and the angle unit with the largest echo frequency domain angle signal power as the center, thereby obtaining the echo signal with the distance v, the angle u and the time window m after the distance dimension fast Fourier transform and the angle dimension fast Fourier transformThe method comprises the steps of carrying out a first treatment on the surface of the Extracting echo signal->And extracting a phase signal of the slow time dimension signal.
In this embodiment, the antenna dimension FFT is used to process difference information between multiple transmitting and receiving antennas in the MIMO radar system, and by performing FFT transformation on signals received on the multiple antennas, characteristics such as relative time delay and frequency offset between different antennas can be extracted, and these information are used to implement functions such as beam forming, interference cancellation, and multi-channel parameter estimation; the beamforming is to perform weighting processing on the received data to form a certain beam shape, allow the information of the direction of the signal of interest to pass through, and form gain for the information of the direction of the signal of interest, and inhibit the signal of the direction of no interest; the improvement of the detection accuracy of the vital signs of the human body is realized.
Further, the specific acquisition process of the distance unit with the smallest distance signal and the distance unit with the largest distance signal in the echo frequency domain is as follows: the index of the distance cell is denoted by V,representing echo frequency domain distance signals with distance v, angle u and time window m after distance dimension fast Fourier transform and angle dimension fast Fourier transform, and obtaining a distance unit with highest power according to a power value through a formula>The specific calculation formula is as follows:
wherein M is the maximum value of the time window, +.>The number of points for the antenna dimension fast fourier transform; for->Is +.>Selecting distance units to obtain minimum distance unit index +.>And maximum distance element index->。
In the present embodiment, in the MIMO radar, the size or the number of the antenna dimension fast fourier transform (Fast Fourier Transform, FFT) refers to the number of sample points or discrete frequency points used when FFT is performed on the received plurality of antenna signals, that is, the size of frequency domain data obtained when signals of the plurality of receiving antennas are converted into a frequency domain representation; arg is an english abbreviation for the argument argule,
such as a function，/>Means when->When the minimum value is obtained, the values of the variables x and y are taken; It means when->When the maximum value is obtained, the values of the variables x and y are taken; a more intuitive description of the distance unit is achieved.
Further, the specific acquisition process of the minimum angle unit and the maximum angle unit of the echo frequency domain angle signal is as follows: the index of the angular unit is denoted by U,representing echo frequency domain angle signals with distance v, angle u and time window m after distance dimension fast Fourier transform and angle dimension fast Fourier transform, and selecting an angle unit with highest power according to a power value through a formula>The specific calculation formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the For->Is +.>Selecting distance units to obtain minimum angle unit index +.>And maximum angular element index +.>。
In the present embodiment, when the number of distance and angle units is selected, i.e.And->It can be set according to an empirical value that at a larger distance, the number of angle units containing the object under test is smaller, while at a lower distance, more angle units are needed to cover the whole object under test; bonding ofThe position information of the tested person is determined through the distance and angle units, so that the probability of false estimation of heart rate values and respiratory values can be reduced; a more intuitive description of the distance unit is achieved.
Further, as shown in fig. 3, in an overall flowchart for estimating a respiratory value and a heart rate value of a measured object according to an embodiment of the present application, a specific estimation process of the respiratory value of the measured object is as follows: s311, calculating the respiration estimated value of each distance-angle unit of the measured object by selecting the maximum value position of the Doppler frequency spectrum in the preset respiration frequency range, and obtaining a respiration matrix R by the respiration estimated value of each distance-angle unit; s312, constructing a respiration pseudo spectrum according to the respiration estimated value in the recorded respiration matrix R and the occurrence frequency of the respiration estimated value, and taking the respiration estimated value with the highest occurrence frequency in the respiration pseudo spectrum as the respiration value of the tested object.
In this embodiment, the respiratory value and the heart rate value of the measured object are estimated to be the second part of the human vital sign detection, that is, the local distance-angle unit is subjected to signal processing in the slow time dimension, so as to obtain the vital sign; the respiratory rate estimate is calculated by considering the frequency region corresponding to the preset respiratory rate range (6-48 bpm), the respiratory matrix R is of the sizeThe method comprises the steps of carrying out a first treatment on the surface of the In radar signal processing, the doppler spectrum is used to analyze the doppler shift in the echo signal; the Doppler spectrum is the result of converting the received pulse sequence into the frequency domain, and represents the intensity of different Doppler frequency components; the maximum position of the Doppler frequency spectrum is calculated by a signal processing algorithm of the radar system; in the respiration pseudo spectrum, each respiration estimated value is taken as a horizontal axis, and the occurrence frequency of each respiration estimated value is taken as a vertical axis; the method realizes more accurate acquisition of the respiration value of the tested object.
Further, the specific estimation process of the heart rate value of the measured object is as follows: s321, calculating heart rate estimated values of all distance-angle units of the measured object by selecting the maximum value position of the Doppler frequency spectrum in a preset heart rate frequency range, and obtaining a heart rate matrix H by the heart rate estimated values of all the distance-angle units; s322, judging whether each heart rate estimated value in the heart rate matrix H and the respiration estimated value in the respiration matrix R have a multiple relation: if the heart rate estimated value has a multiple relation with the respiration estimated value in the respiration matrix R, ignoring the heart rate estimated value, and continuing to judge the next heart rate estimated value; if the heart rate estimation value has no multiple relation with the respiration estimation value in the respiration matrix R, S323 is executed; s323, recording a heart rate estimated value and the occurrence frequency of the heart rate estimated value when the heart rate estimated value and the respiration estimated value in the respiration matrix R have no multiple relation, and constructing a heart rate pseudo spectrum according to the recorded heart rate estimated value and the occurrence frequency of the heart rate estimated value in the heart rate matrix H; s324, obtaining a heart rate estimated value with highest occurrence frequency in a heart rate pseudo spectrum, and judging whether the heart rate estimated value is smaller than a preset heart rate: if the heart rate estimated value is smaller than the preset heart rate, the heart rate estimated value is the heart rate value of the tested object; if the heart rate estimation value is not less than the preset heart rate, performing S325; s325, regenerating a reference heart rate pseudo spectrum through a heart rate harmonic product spectrum, acquiring a reference heart rate estimated value with highest occurrence frequency in the reference heart rate pseudo spectrum, and executing S324 until obtaining the heart rate value of the tested object.
In this embodiment, the preset heart rate frequency range is (48-120 bpm); the heart rate matrix H has a size ofThe method comprises the steps of carrying out a first treatment on the surface of the In the heart rate pseudo spectrum, each heart rate estimated value is taken as a horizontal axis, and the occurrence frequency of each heart rate estimated value is taken as a vertical axis; in some cases, the highest spectral component in the heart rate pseudo spectrum is exactly the harmonic of the heart rate itself, thus setting the preset heart rate to 100bpm; under normal conditions, a certain correlation exists between the breathing cycle and the heartbeat cycle; ignoring heart rate estimates that have a multiple of the breath estimates in the breath matrix R corrects for breath harmonics that may appear on the heart rate frequency; the harmonic product spectrum generally refers to the periodic non-sinusoidal electric quantity which is subjected to Fourier series decomposition, and the electric quantity generated by the current which is larger than the fundamental wave frequency is called as harmonic wave except the electric quantity of the fundamental wave frequency; the heart rate value of the tested object can be obtained more accurately.
Further, as shown in FIG. 4, the present application isThe schematic diagram for generating the heart rate harmonic product spectrum provided by the embodiment of the application is shown in fig. 5, which is an example diagram of the frequency spectrum and the harmonic product spectrum of the sine wave with the analog fundamental frequency of 1Hz provided by the embodiment of the application, and the specific construction process of the heart rate harmonic product spectrum is as follows: according to the discrete Fourier transform algorithm, performing discrete Fourier transform processing on the heart rate pseudo spectrum, and extracting heart rate estimated values for multiplication to obtain a heart rate harmonic product spectrum Heart rate harmonic product spectrum->The specific calculation formula of (2) is as follows:
wherein q is the decimated magnitude spectrum +.>Is (are) extracted by the extraction rate of->Q is a preset harmonic number, and f is a heart rate frequency.
In this embodiment, the harmonic product spectrum (Harmonic Product Spectrum, HPS) is generated by the decimated multiplication of the original magnitude spectrum; the highest frequency value in the harmonic product spectrum is called the fundamental frequency of the harmonic product spectrum; harmonics exist at integer multiples of the fundamental frequency; as can be seen from fig. 5, the harmonic components are aligned with the fundamental frequency at each extraction, their product enhances the amplitude of the fundamental frequency components, and the number of harmonic components to be considered depends on the system sampling rate and the harmonic number; the harmonic product spectrum corrects the frequency spectrum influence of heart rate harmonic waves, so that the detection performance of vital signs can be improved, including the detection accuracy and stability; discrete fourier transform (Discrete Fourier Transform, DFT), i.e. a fourier series of discrete signals with periodic characteristics, i.e. truncating discrete signals of indefinite length to a certain number of sampling points, and then periodically extending these sampling points into periodic signals; the heart rate value of the tested object can be obtained more accurately.
Further, a reference heart rate pseudo spectrum is generated, and a reference heart rate estimated value with highest occurrence frequency in the reference heart rate pseudo spectrum is obtained, wherein the specific process is as follows: the specific generation process of the reference heart rate pseudo spectrum comprises the following steps: by selecting heart rate harmonic product spectrum within preset heart rate frequency range Calculating the reference heart rate estimated value of each distance-angle unit of the measured object, and obtaining the heart rate harmonic product spectrum of the extraction rate q from the reference heart rate estimated value of each distance-angle unit>Is>The method comprises the steps of carrying out a first treatment on the surface of the Judging reference heart rate matrix->Whether the reference heart rate estimated value and the respiration estimated value in the respiration matrix R have a multiple relation: if the reference heart rate estimated value has a multiple relation with the respiration estimated value in the respiration matrix R, ignoring the reference heart rate estimated value, continuously judging the next reference heart rate estimated value, otherwise, recording the frequency of occurrence of the reference heart rate estimated value and the reference heart rate estimated value; according to the recorded reference heart rate matrix->Constructing a reference heart rate pseudo spectrum according to the reference heart rate estimated value and the occurrence frequency of the reference heart rate estimated value; the specific acquisition process of the reference heart rate estimated value with the highest occurrence frequency in the reference heart rate pseudo spectrum is as follows: harmonic product spectrum of heart rate->Analyzing, namely obtaining a reference heart rate estimated value (which is highest in occurrence frequency) in a heart rate harmonic product spectrum through a formula in a preset heart rate frequency range>Reference heart rate estimate +.>The specific calculation formula of (2) is。
In the present embodiment, in general, harmonic product spectrum Fundamental frequency of->The specific calculation formula of (2) is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the When the harmonic product spectrum HPS is applied to human vital sign extraction, the respiratory amplitude is obviously larger than the heart rate amplitude, so the heart rate harmonic product spectrum +.>The basic frequency search of (2) is performed at a preset heart rate frequency of +.>The method comprises the steps of carrying out a first treatment on the surface of the The heart rate value of the tested object can be obtained more accurately.
Further, the specific calculation process of the human vital sign detection efficiency is as follows: based on calculating the single distance unit and the distance unit with highest powerTime for distance of +.>And calculating the angle unit with the highest power and the single angle unit +.>Time taken for distance +.>Combining the number of distance units/>And the number of angle units->Time for obtaining echo signal +.>The reference time for obtaining the echo signal is +.>The method comprises the steps of carrying out a first treatment on the surface of the Acquiring time +.>And the post-processing time of the echo signal +.>The corresponding reference time is respectivelyAnd->The method comprises the steps of carrying out a first treatment on the surface of the The human vital sign detection efficiency DP is calculated through a formula, and the specific calculation formula is as follows:
wherein e is a natural constant, < >>And->And obtaining a correction factor of the time for obtaining the echo signal and a correction factor of the total time of the distance-angle unit of the measured object by post-processing of the echo signal and extracting the correction factors of the time for obtaining the echo signal and the total time of the distance-angle unit of the measured object respectively for the number of the distance units and the angle units.
In this embodiment, the greater the number of distance units and angle units, the longer the corresponding time for obtaining the echo signal, and the lower the human vital sign detection efficiency; similarly, the longer the time for acquiring the distance-angle unit of the measured object and the post-processing time of the echo signal, the lower the human vital sign detection efficiency; the ratio of the time for extracting the distance-angle unit of the measured object and the post-processing time of the echo signal to the total time is larger, the influence on the detection efficiency of the vital sign of the human body is larger, and when the independent variable is smaller than 1 for logarithmic functions with the base number larger than 0 and smaller than 1, the larger the base number is, the larger the corresponding function value is; the human vital sign detection efficiency is comprehensively estimated.
The technical scheme provided by the embodiment of the application at least has the following technical effects or advantages: relative to the bulletin number: according to the human vital sign detection method of the millimeter wave radar in the complex indoor scene disclosed by CN114742117B, the echo time domain signal is obtained through the average processing of the received initial echo signal in the slow time dimension and the fast time dimension, then the distance information of the detected object is obtained through the combination of the distance dimension fast Fourier transform processing, then the angle information of the detected object is obtained through the combination of the angle dimension fast Fourier transform processing, a plurality of distance-angle units of the detected object are obtained according to the comparison result of the reference distance information and the angle information, and the respiratory value and the heart rate value of the detected object are obtained through the processing of the extracted distance-angle units, so that the stable detection of the human vital sign is realized, and the improvement of the detection stability of the human vital sign is realized; relative to the bulletin number: according to the multi-target respiratory heart rate monitoring method and system based on the millimeter wave radar technology disclosed by CN111481184B, the minimum distance unit and the maximum distance unit of the echo frequency domain distance signal are obtained according to the number of points of the antenna dimension fast Fourier transform, then the minimum angle unit and the maximum angle unit of the echo frequency domain angle signal are obtained by combining the minimum distance unit and the maximum distance unit of the obtained echo frequency domain distance signal, so that the echo signal after the distance dimension fast Fourier transform and the angle dimension fast Fourier transform is obtained, the corresponding phase signal is further obtained through the slow time dimension signal of the extracted echo signal, the Doppler frequency spectrum is obtained through processing, and finally the heart rate value and the respiratory value of a measured object are obtained through analysis of the Doppler frequency spectrum, so that the fast detection of the vital sign of a human body is realized, and the improvement of the detection efficiency of the vital sign of the human body is realized.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. The human vital sign detection method based on MIMO radar and beam forming is used for a server and is characterized by comprising the following steps:
s1, a receiving station of the MIMO radar receives initial echo signals reflected back by a radiation station after transmitting waveforms to a measured object through a preset number of antennas from different angles and distances in a preset time window, and then performs preprocessing operation on the initial echo signals to extract distance-angle units of the measured object;
s2, extracting phase signals of a distance-angle unit of the measured object, then performing unwrapping and differential processing on the phase signals of the distance-angle unit of the measured object, and performing fast Fourier transform processing by combining a fast Fourier transform algorithm to obtain Doppler frequency spectrum;
s3, analyzing the acquired Doppler frequency spectrum, screening the Doppler frequency spectrum to obtain a respiratory signal and a heart rate signal, estimating the heart rate value and the respiratory value of the tested object according to the obtained respiratory signal and heart rate signal, detecting the vital sign of the human body of the tested object according to the estimated heart rate value and the respiratory value, and calculating the detection efficiency of the vital sign of the human body;
The pretreatment operation in the step S1 comprises the following specific steps:
s11, static clutter filtering: according to a phasor mean value cancellation algorithm, an intermediate frequency signal of an initial echo signal is removed through average processing in a slow time dimension and a fast time dimension, and an echo time domain signal is obtained;
s12, obtaining distance information: according to a fast Fourier transform algorithm, converting the echo time domain signal into an echo frequency domain distance signal by performing distance dimension fast Fourier transform on the echo time domain signal, and obtaining distance information of the measured object according to the echo frequency domain distance signal;
s13, acquiring angle information: according to a fast Fourier transform algorithm, converting the echo time domain signal into an echo frequency domain angle signal by performing angle dimension fast Fourier transform on the echo time domain signal, and obtaining angle information of the measured object according to the echo frequency domain angle signal;
s14, collecting distance-angle units of the measured object: and comparing the distance information of the measured object and the angle information of the measured object with the reference distance information and the reference angle information to obtain a target distance width and a target angle width, setting a distance window and an angle window according to the target distance width and the target angle width, and collecting distance-angle units of the measured object.
2. The human vital sign detection method based on MIMO radar and beam forming according to claim 1, wherein the specific extraction process of the phase signals of the plurality of distance-angle units of the object to be detected in S2 is as follows:
acquiring a distance unit with highest echo frequency domain distance signal power through a formula according to the antenna dimension fast Fourier transform result, and selecting a distance unit with smallest echo frequency domain distance signal and a distance unit with largest echo frequency domain distance signal by taking the distance unit with highest echo frequency domain distance signal power as a center;
combining the distance unit with the minimum distance signal and the distance unit with the maximum distance signal of the echo frequency domain, obtaining the angle unit with the highest power of the angle signal of the echo frequency domain through a formula, and then obtaining the angle of the echo frequency domainSelecting the angle unit with the highest signal power as the center to select the angle unit with the smallest angle signal and the angle unit with the largest angle signal of the echo frequency domain, thereby obtaining the echo signal with the distance v, the angle u and the time window m after the distance dimension fast Fourier transform and the angle dimension fast Fourier transform；
Extracting echo signalsAnd extracting a phase signal of the slow time dimension signal.
3. The human vital sign detection method based on the MIMO radar and the beam forming as claimed in claim 2, wherein the specific acquisition process of the minimum distance unit and the maximum distance unit of the echo frequency domain distance signal is as follows:
The index of the distance cell is denoted by V,representing echo frequency domain distance signals with distance v, angle u and time window m after distance dimension fast Fourier transform and angle dimension fast Fourier transform, and obtaining a distance unit with highest power according to a power value through a formula>The specific calculation formula is as follows:wherein M is the maximum value of the time window, +.>The number of points for the antenna dimension fast fourier transform;
to the following pair ofIs +.>Selecting distance units to obtain minimum distance unit index +.>And maximum distance element index->。
4. The human vital sign detection method based on the MIMO radar and the beam forming as claimed in claim 3, wherein the specific acquisition process of the minimum angle unit and the maximum angle unit of the echo frequency domain angle signal is as follows:
the index of the angular unit is denoted by U,representing echo frequency domain angle signals with distance v, angle u and time window m after distance dimension fast Fourier transform and angle dimension fast Fourier transform, and selecting an angle unit with highest power according to a power value through a formula>The specific calculation formula is as follows:；
to the following pair ofIs +.>Selecting distance units to obtain minimum angle unit index +. >And maximum angular element index +.>。
5. The human vital sign detection method based on the MIMO radar and the beam forming as claimed in claim 1, wherein the specific estimation process of the respiration value of the detected object is as follows:
s311, calculating the respiration estimated value of each distance-angle unit of the measured object by selecting the maximum value position of the Doppler frequency spectrum in the preset respiration frequency range, and obtaining a respiration matrix R by the respiration estimated value of each distance-angle unit;
s312, constructing a respiration pseudo spectrum according to the respiration estimated value in the recorded respiration matrix R and the occurrence frequency of the respiration estimated value, and taking the respiration estimated value with the highest occurrence frequency in the respiration pseudo spectrum as the respiration value of the tested object.
6. The human vital sign detection method based on the MIMO radar and the beamforming according to claim 5, wherein the specific estimation process of the heart rate value of the measured object is as follows:
s321, calculating heart rate estimated values of all distance-angle units of the measured object by selecting the maximum value position of the Doppler frequency spectrum in a preset heart rate frequency range, and obtaining a heart rate matrix H by the heart rate estimated values of all the distance-angle units;
s322, judging whether each heart rate estimated value in the heart rate matrix H and the respiration estimated value in the respiration matrix R have a multiple relation:
If the heart rate estimated value has a multiple relation with the respiration estimated value in the respiration matrix R, ignoring the heart rate estimated value, and continuing to judge the next heart rate estimated value;
if the heart rate estimation value has no multiple relation with the respiration estimation value in the respiration matrix R, S323 is executed;
s323, recording a heart rate estimated value and the occurrence frequency of the heart rate estimated value when the heart rate estimated value and the respiration estimated value in the respiration matrix R have no multiple relation, and constructing a heart rate pseudo spectrum according to the recorded heart rate estimated value and the occurrence frequency of the heart rate estimated value in the heart rate matrix H;
s324, obtaining a heart rate estimated value with highest occurrence frequency in a heart rate pseudo spectrum, and judging whether the heart rate estimated value is smaller than a preset heart rate:
if the heart rate estimated value is smaller than the preset heart rate, the heart rate estimated value is the heart rate value of the tested object;
if the heart rate estimation value is not less than the preset heart rate, performing S325;
s325, regenerating a reference heart rate pseudo spectrum through a heart rate harmonic product spectrum, acquiring a reference heart rate estimated value with highest occurrence frequency in the reference heart rate pseudo spectrum, and executing S324 until obtaining the heart rate value of the tested object.
7. The human vital sign detection method based on the MIMO radar and the beam forming as set forth in claim 6, wherein the specific construction process of the heart rate harmonic product spectrum is as follows:
According to the discrete Fourier transform algorithm, performing discrete Fourier transform processing on the heart rate pseudo spectrum, and extracting heart rate estimated values for multiplication to obtain a heart rate harmonic product spectrumHeart rate harmonic product spectrum->The specific calculation formula of (2) is as follows:
wherein q is the decimated magnitude spectrum +.>Is (are) extracted by the extraction rate of->Q is a preset harmonic number, and f is a heart rate frequency.
8. The human vital sign detection method based on the MIMO radar and the beam forming as set forth in claim 7, wherein the steps of generating the reference heart rate pseudo spectrum and obtaining the reference heart rate estimated value with the highest occurrence frequency in the reference heart rate pseudo spectrum are as follows:
the specific generation process of the reference heart rate pseudo spectrum comprises the following steps:
by selecting heart rate harmonic product spectrum within preset heart rate frequency rangeCalculating the reference heart rate estimated value of each distance-angle unit of the measured object, and obtaining the heart rate harmonic product spectrum of the extraction rate q from the reference heart rate estimated value of each distance-angle unit>Is>；
Judging reference heart rate matrixWhether the reference heart rate estimated value and the respiration estimated value in the respiration matrix R have a multiple relation:
if the reference heart rate estimated value has a multiple relation with the respiration estimated value in the respiration matrix R, ignoring the reference heart rate estimated value, continuously judging the next reference heart rate estimated value, otherwise, recording the frequency of occurrence of the reference heart rate estimated value and the reference heart rate estimated value;
From a recorded reference heart rate matrixConstructing a reference heart rate pseudo spectrum according to the reference heart rate estimated value and the occurrence frequency of the reference heart rate estimated value;
the specific acquisition process of the reference heart rate estimated value with the highest occurrence frequency in the reference heart rate pseudo spectrum is as follows:
harmonic product spectrum of heart rateAnalyzing, namely obtaining a reference heart rate estimated value (which is highest in occurrence frequency) in a heart rate harmonic product spectrum through a formula in a preset heart rate frequency range>Reference heart rate estimate +.>The specific calculation formula of (2) is as follows:。
9. the human vital sign detection method based on the MIMO radar and the beam forming as claimed in claim 1, wherein the specific calculation process of the human vital sign detection efficiency is as follows:
based on calculating the single distance unit and the distance unit with highest powerTime for distance of +.>And calculating the angle unit with the highest power and the single angle unit +.>Time taken for distance +.>The number of combined distance units->And the number of angle units->Time for obtaining echo signal +.>Obtaining the reference time of the echo signal as follows；
Obtaining time for extracting distance-angle unit of measured objectAnd the post-processing time of the echo signal +.>The corresponding reference times are +. >And->；
The human vital sign detection efficiency DP is calculated through a formula, and the specific calculation formula is as follows:
wherein e is a natural constant, < >>And->And obtaining a correction factor of the time for obtaining the echo signal and a correction factor of the total time of the distance-angle unit of the measured object by post-processing of the echo signal and extracting the correction factors of the time for obtaining the echo signal and the total time of the distance-angle unit of the measured object respectively for the number of the distance units and the angle units.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016161409A (en) * | 2015-03-02 | 2016-09-05 | 株式会社東芝 | Radar system and radar signal processing method of the same |
| CN112674740A (en) * | 2020-12-22 | 2021-04-20 | 北京工业大学 | Vital sign detection method based on millimeter wave radar |
| WO2021109526A1 (en) * | 2019-12-03 | 2021-06-10 | 南京汇君半导体科技有限公司 | Vital sign signal enhancement method and device, and extraction method and device based on millimeter-wave radar |
| CN113534141A (en) * | 2021-07-01 | 2021-10-22 | 深圳晶华相控科技有限公司 | Remote vital sign detection method and device based on phased array radar technology |
| WO2022104868A1 (en) * | 2020-11-19 | 2022-05-27 | 华中师范大学 | Millimeter wave radar-based non-contact real-time vital sign monitoring system and method |
| CN114847911A (en) * | 2022-05-25 | 2022-08-05 | 江苏科技大学 | Multi-person vital sign monitoring method based on millimeter wave radar |
| CN115089143A (en) * | 2022-08-24 | 2022-09-23 | 江苏亿连通信技术有限公司 | Millimeter wave radar vital sign signal extraction and measurement method |
| CN115399747A (en) * | 2022-08-15 | 2022-11-29 | 电子科技大学 | Multi-person vital sign monitoring method based on millimeter wave radar |
| WO2023004688A1 (en) * | 2021-07-29 | 2023-02-02 | 南京浙溧智能制造研究院有限公司 | Non-contact respiration monitoring method based on doppler radar |
| CN116500620A (en) * | 2023-03-28 | 2023-07-28 | 新石器慧通（北京）科技有限公司 | Data processing method and device of millimeter wave radar, storage medium and unmanned vehicle |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2265169A4 (en) * | 2008-04-03 | 2013-01-09 | Kai Medical Inc | Non-contact physiologic motion sensors and methods for use |
| US11051702B2 (en) * | 2014-10-08 | 2021-07-06 | University Of Florida Research Foundation, Inc. | Method and apparatus for non-contact fast vital sign acquisition based on radar signal |
-
2023
- 2023-08-30 CN CN202311105525.7A patent/CN116840805B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016161409A (en) * | 2015-03-02 | 2016-09-05 | 株式会社東芝 | Radar system and radar signal processing method of the same |
| WO2021109526A1 (en) * | 2019-12-03 | 2021-06-10 | 南京汇君半导体科技有限公司 | Vital sign signal enhancement method and device, and extraction method and device based on millimeter-wave radar |
| WO2022104868A1 (en) * | 2020-11-19 | 2022-05-27 | 华中师范大学 | Millimeter wave radar-based non-contact real-time vital sign monitoring system and method |
| CN112674740A (en) * | 2020-12-22 | 2021-04-20 | 北京工业大学 | Vital sign detection method based on millimeter wave radar |
| CN113534141A (en) * | 2021-07-01 | 2021-10-22 | 深圳晶华相控科技有限公司 | Remote vital sign detection method and device based on phased array radar technology |
| WO2023004688A1 (en) * | 2021-07-29 | 2023-02-02 | 南京浙溧智能制造研究院有限公司 | Non-contact respiration monitoring method based on doppler radar |
| CN114847911A (en) * | 2022-05-25 | 2022-08-05 | 江苏科技大学 | Multi-person vital sign monitoring method based on millimeter wave radar |
| CN115399747A (en) * | 2022-08-15 | 2022-11-29 | 电子科技大学 | Multi-person vital sign monitoring method based on millimeter wave radar |
| CN115089143A (en) * | 2022-08-24 | 2022-09-23 | 江苏亿连通信技术有限公司 | Millimeter wave radar vital sign signal extraction and measurement method |
| CN116500620A (en) * | 2023-03-28 | 2023-07-28 | 新石器慧通（北京）科技有限公司 | Data processing method and device of millimeter wave radar, storage medium and unmanned vehicle |
Non-Patent Citations (2)
| Title |
|---|
| "Non-contact Non-invasive Heart and Respiration Rates Monitoring with MIMO Radar Sensing";Qiwei Liu等;《2018 IEEE Global Communications Conference (GLOBECOM)》;1-6 * |
| "基于毫米波雷达的动态目标生命体征检测研究";琚泽东等;《电子测量技术》;第45卷(第16期);27-33 * |
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